{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression on MNIST\n",
    "\n",
    "This is MLP (784-10) on MNIST. Adam algorithm (lr=0.001) with 100 epoches.\n",
    "\n",
    "\n",
    "#### Original Method\n",
    "\n",
    "    Total params: 7,850\n",
    "    Trainable params: 7,850\n",
    "    Non-trainable params: 0\n",
    "\n",
    "    \n",
    "####  LR with 7840 intrinsic dim    \n",
    "    Total params: 61,559,690\n",
    "    Trainable params: 7,840\n",
    "    Non-trainable params: 61,551,850 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 is in../results/lr_mnist/170705_212527_ae92905_master_fnn_mnist_10/diary\n",
      "50 is in../results/lr_mnist/170705_212640_ae92905_master_fnn_mnist_50/diary\n",
      "100 is in../results/lr_mnist/170705_212744_ae92905_master_fnn_mnist_100/diary\n",
      "300 is in../results/lr_mnist/170705_212848_ae92905_master_fnn_mnist_300/diary\n",
      "500 is in../results/lr_mnist/170705_212952_ae92905_master_fnn_mnist_500/diary\n",
      "1000 is in../results/lr_mnist/170705_213059_ae92905_master_fnn_mnist_1000/diary\n",
      "2000 is in../results/lr_mnist/170705_213208_ae92905_master_fnn_mnist_2000/diary\n",
      "3000 is in../results/lr_mnist/170705_213317_ae92905_master_fnn_mnist_3000/diary\n",
      "4000 is in../results/lr_mnist/170705_213435_ae92905_master_fnn_mnist_4000/diary\n",
      "5000 is in../results/lr_mnist/170705_213600_ae92905_master_fnn_mnist_5000/diary\n",
      "6000 is in../results/lr_mnist/170705_213731_ae92905_master_fnn_mnist_6000/diary\n",
      "7000 is in../results/lr_mnist/170705_213908_ae92905_master_fnn_mnist_7000/diary\n",
      "7840 is in../results/lr_mnist/170705_214047_ae92905_master_fnn_mnist_7840/diary\n",
      "7850 is in../results/lr_mnist/170705_222631_ae92905_master_fnn_mnist_7850/diary\n",
      "['../results/lr_mnist/170705_212527_ae92905_master_fnn_mnist_10/diary', '../results/lr_mnist/170705_212640_ae92905_master_fnn_mnist_50/diary', '../results/lr_mnist/170705_212744_ae92905_master_fnn_mnist_100/diary', '../results/lr_mnist/170705_212848_ae92905_master_fnn_mnist_300/diary', '../results/lr_mnist/170705_212952_ae92905_master_fnn_mnist_500/diary', '../results/lr_mnist/170705_213059_ae92905_master_fnn_mnist_1000/diary', '../results/lr_mnist/170705_213208_ae92905_master_fnn_mnist_2000/diary', '../results/lr_mnist/170705_213317_ae92905_master_fnn_mnist_3000/diary', '../results/lr_mnist/170705_213435_ae92905_master_fnn_mnist_4000/diary', '../results/lr_mnist/170705_213600_ae92905_master_fnn_mnist_5000/diary', '../results/lr_mnist/170705_213731_ae92905_master_fnn_mnist_6000/diary', '../results/lr_mnist/170705_213908_ae92905_master_fnn_mnist_7000/diary', '../results/lr_mnist/170705_214047_ae92905_master_fnn_mnist_7840/diary', '../results/lr_mnist/170705_222631_ae92905_master_fnn_mnist_7850/diary']\n",
      "LR model:\n",
      "(0.272924, 0.9278, 0.226459, 0.93676, 54.8907)\n",
      "\n",
      "10 dim:\n",
      "(2.2339, 0.2073, 2.24105, 0.1991, 59.6453)\n",
      "\n",
      "50 dim:\n",
      "(1.90171, 0.3613, 1.93499, 0.35024, 58.837)\n",
      "\n",
      "100 dim:\n",
      "(1.52765, 0.4955, 1.57903, 0.47464, 58.9677)\n",
      "\n",
      "300 dim:\n",
      "(0.734115, 0.7656, 0.77054, 0.75776, 59.9712)\n",
      "\n",
      "500 dim:\n",
      "(0.514295, 0.8424, 0.548535, 0.8326, 61.7267)\n",
      "\n",
      "1000 dim:\n",
      "(0.360117, 0.8945, 0.377106, 0.89042, 63.6534)\n",
      "\n",
      "2000 dim:\n",
      "(0.299441, 0.9156, 0.299516, 0.91572, 64.5393)\n",
      "\n",
      "3000 dim:\n",
      "(0.283106, 0.92, 0.276267, 0.92256, 73.1696)\n",
      "\n",
      "4000 dim:\n",
      "(0.27753, 0.922, 0.264478, 0.92568, 79.3252)\n",
      "\n",
      "5000 dim:\n",
      "(0.273176, 0.9237, 0.25732, 0.92788, 86.1009)\n",
      "\n",
      "6000 dim:\n",
      "(0.271979, 0.9238, 0.252969, 0.92944, 91.4728)\n",
      "\n",
      "7000 dim:\n",
      "(0.272981, 0.9241, 0.249615, 0.93016, 93.8936)\n",
      "\n",
      "7840 dim:\n",
      "(0.272847, 0.9235, 0.247482, 0.93114, 100.07)\n",
      "\n",
      "7850 dim:\n",
      "(0.272842, 0.9241, 0.247454, 0.93116, 99.2464)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results_dir = '../results'\n",
    "\n",
    "\n",
    "class Results():\n",
    "    def __init__(self):\n",
    "        self.train_loss     = []\n",
    "        self.train_accuracy = []\n",
    "        self.train_loss = []\n",
    "        self.valid_loss = []\n",
    "        self.run_time   = []\n",
    "        \n",
    "    def add_entry(self, train_loss, train_accuracy, valid_loss, valid_accuracy, run_time):\n",
    "        self.train_loss.append(train_loss)\n",
    "        self.train_accuracy.append(train_accuracy)\n",
    "        self.train_loss.append(train_loss)\n",
    "        self.valid_loss.append(valid_loss)\n",
    "        self.run_time.append(run_time)\n",
    "      \n",
    "    def add_entry_list(self, entry):\n",
    "        self.add_entry(entry[0], entry[1], entry[2], entry[3], entry[4])\n",
    "        \n",
    "    def list2np(self):\n",
    "        self.train_loss     = []\n",
    "        self.train_accuracy = []\n",
    "        self.train_loss = []\n",
    "        self.valid_loss = []\n",
    "        self.run_time   = []        \n",
    "\n",
    "dim = [10, 50, 100, 300, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 7840, 7850]\n",
    "i = 0        \n",
    "\n",
    "# filename list of diary\n",
    "diary_names = []\n",
    "for subdir, dirs, files in os.walk(results_dir):\n",
    "    for file in files:\n",
    "        if file == 'diary':\n",
    "            fname = os.path.join(subdir, file)\n",
    "            diary_names.append(fname)\n",
    "\n",
    "            \n",
    "diary_names_ordered = []\n",
    "for d in dim:\n",
    "    for f in diary_names:\n",
    "        if ('_'+str(d)+'/' in f) and ('fnn_mnist_200_200' not in f):\n",
    "            print \"%d is in\" % d + f\n",
    "            diary_names_ordered.append(f)\n",
    "        if '170705_214554_ae92905_master_mnist_fnn_dir/' in f:\n",
    "            diary_names_dir = f\n",
    "         \n",
    "print diary_names_ordered    \n",
    "        \n",
    "# extrinsic update  method\n",
    "with open(diary_names_dir,'r') as ff:\n",
    "    lines0 = ff.readlines()\n",
    "    R_dir = extract_num(lines0)\n",
    "\n",
    "print \"LR model:\\n\" + str(R_dir) + \"\\n\"\n",
    "\n",
    "# intrinsic update method\n",
    "Rs = []\n",
    "i = 0\n",
    "for fname in diary_names_ordered:\n",
    "    with open(fname,'r') as ff:\n",
    "        lines0 = ff.readlines()\n",
    "        R = extract_num(lines0)\n",
    "        print \"%d dim:\\n\"%dim[i] + str(R) + \"\\n\"\n",
    "        i += 1\n",
    "\n",
    "        Rs.append(R)\n",
    "                            \n",
    "Rs = np.array(Rs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_num(lines0):\n",
    "\n",
    "    valid_loss_str     = lines0[-5]\n",
    "    valid_accuracy_str = lines0[-6]\n",
    "    train_loss_str     = lines0[-8]\n",
    "    train_accuracy_str = lines0[-9]\n",
    "    run_time_str       = lines0[-10]\n",
    "\n",
    "    valid_loss     = float(valid_loss_str.split( )[-1])\n",
    "    valid_accuracy = float(valid_accuracy_str.split( )[-1])\n",
    "    train_loss     = float(train_loss_str.split( )[-1])\n",
    "    train_accuracy = float(train_accuracy_str.split( )[-1])\n",
    "    run_time       = float(run_time_str.split( )[-1])\n",
    "    \n",
    "    return valid_loss, valid_accuracy, train_loss, train_accuracy, run_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison with Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BgYEsWbLkhMe7/vrrAbjhhhuYNGkSAOnp6QwfPpzMzEwKCwvp0qULAOeffz733HMPN9xw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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a26e2dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = Rs.shape[0]\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,0],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[0]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,2],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[2]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "\n",
    "ax.scatter(dim, Rs[:,0])\n",
    "ax.scatter(dim, Rs[:,2])\n",
    "\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss')\n",
    "ax.set_title('Cross Entropy  Loss')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_ylim([0.1,0.8])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fsa2gbuMqDcEtOeS70j1TBvmudNyev8XXFW1LJz/o6HX57j24XUfID8kkLzgP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\nc7E3MzPrcC72ZmZmHc7F3szMrMP9PyTigZO787xxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a26d48410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,1],'b-', label=\"Testing\")\n",
    "ax.plot(dim, R_dir[1]*np.ones(N),'b-', label=\"Testing: baseline\")\n",
    "ax.plot(dim, Rs[:,3],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[3]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,1])\n",
    "ax.scatter(dim, Rs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Accuracy')\n",
    "ax.set_title('Cross Entropy  Accuracy')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_ylim([0.75,1.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RI0aOHElubi5xcXEAdOnSheTkZD755BP69+/PPffcQ5UqVejatSvvvPNOkde5O3/hGpxz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\nA0BDAh4AGhLwANCQgAeAhs4qlvKFQAjepQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a26beeb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, Rs[:,4],'g-', label=\"Training\")\n",
    "ax.plot(dim, R_dir[4]*np.ones(N),'g-', label=\"Training: baseline\")\n",
    "ax.scatter(dim, Rs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Per Dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.23390000e-01   2.07300000e-02   2.24105000e-01   1.99100000e-02\n",
      "    5.96453000e+00]\n",
      " [  3.80342000e-02   7.22600000e-03   3.86998000e-02   7.00480000e-03\n",
      "    1.17674000e+00]\n",
      " [  1.52765000e-02   4.95500000e-03   1.57903000e-02   4.74640000e-03\n",
      "    5.89677000e-01]\n",
      " [  2.44705000e-03   2.55200000e-03   2.56846667e-03   2.52586667e-03\n",
      "    1.99904000e-01]\n",
      " [  1.02859000e-03   1.68480000e-03   1.09707000e-03   1.66520000e-03\n",
      "    1.23453400e-01]\n",
      " [  3.60117000e-04   8.94500000e-04   3.77106000e-04   8.90420000e-04\n",
      "    6.36534000e-02]\n",
      " [  1.49720500e-04   4.57800000e-04   1.49758000e-04   4.57860000e-04\n",
      "    3.22696500e-02]\n",
      " [  9.43686667e-05   3.06666667e-04   9.20890000e-05   3.07520000e-04\n",
      "    2.43898667e-02]\n",
      " [  6.93825000e-05   2.30500000e-04   6.61195000e-05   2.31420000e-04\n",
      "    1.98313000e-02]\n",
      " [  5.46352000e-05   1.84740000e-04   5.14640000e-05   1.85576000e-04\n",
      "    1.72201800e-02]\n",
      " [  4.53298333e-05   1.53966667e-04   4.21615000e-05   1.54906667e-04\n",
      "    1.52454667e-02]\n",
      " [  3.89972857e-05   1.32014286e-04   3.56592857e-05   1.32880000e-04\n",
      "    1.34133714e-02]\n",
      " [  3.48019133e-05   1.17793367e-04   3.15665816e-05   1.18767857e-04\n",
      "    1.27640306e-02]\n",
      " [  3.47569427e-05   1.17719745e-04   3.15228025e-05   1.18619108e-04\n",
      "    1.26428535e-02]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QMzOzIuqxZR8Rc4B3AjMjogV4GTg678AqkQfomZlZEWUZoPchoCUi2iRdBFwP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\nsTczM6tyLvZmZmZV7v8DjZkg8WK7cQQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a273bb290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M8VS1jgF6vmZvZmY5kuXL4r+S9AiwCfiMpN2AzeUNqzq5G9/MzPKo15Z9RMwD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\nAUUFwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a26f64410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NRs = Rs/np.array(dim).reshape(N,1)\n",
    "print NRs\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,0],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,0])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,2],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,2])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bx4OCBatbaairoEHwxSdW0zirZtN2Em+a7mxdKba7+28ruXj+EzQ1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\nrEy5CDAzMytT/x+IJFRVcx+tngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a26e2d690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2ocDPgEOBtcDJEfG0pDHALcDbgJ9GxDkF+xwK/BSoARYC/xoRIWk34CZgEvA0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a27397650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,1],'b-', label=\"Testing\")\n",
    "ax.scatter(dim, NRs[:,1])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)\n",
    "\n",
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,3],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,3])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Cross Entropy Loss per dim')\n",
    "ax.set_title('Cross Entropy  Loss per Dim')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WVQOQYT+GtlxMS+ZVtlT8omO+iIhIuUldsb586hiqK7MAVLc3UNM6hS0Vv+TD\nk9pLHJmIiEjnUlesz5lQzzXnHkN9XTUGjN3vswweOJi7l3+Ztva2UocnIiKyh9SdDQ5Rwc4/83vm\ns9s5/9fnc+P8G/nCpC+UMDIREZE9pW5k3ZmPHP0R3n/U+/nynC/z0qaXSh2OiIjILlSsATPjh2f+\nkIpMBdPvm467lzokERGRDirWsVGDR/Gt936LP778R+546o5ShyMiItJBxTrP9InTOenQk7j095ey\npnFNqcMREREBVKx3kbEMt551K80tzXzmgc+UOhwRERFAxXoPRw09im9M/gazls7i10t+XepwRERE\nVKw7c+l7LmXCiAlc/LuL2dS8qdThiIhIyqXyOuvuVGQquO3s2zj21mM5755Ps239dFZvbmZkXTWX\nTx2jp3OJiEif0sh6LyYcPIEPHPEp5qz4OS+88TgOrNrczIxZz+j51yIi0qdUrLuw6tUzqWivZ2Pl\n99iWWYrTpsdpiohIn1MbvAvrtrQzNPM51g/4KusGXk7Gh1DddixvvnEcTTuOo3ZAbceysxet4rqH\nlqldLiIiRadi3YWRddWs2nw09dv+i23ZhbyZeYI3s4+zteIPHPit73Dq4ady1lFnUbmjgW8/8BrN\nLdGDQHLtckAFW0RE9pmKdRcunzqGGbOeobmllpq2k6lpO5kqnAtOepPX2x7n3r/dy++W/w6AAZkj\nqa44lkp/C1nfn5bW/bn2wVY+MH4kZlbiTEREJGQq1l3IjYo7b29/mO9O/S5LX1vK8Td8i+bME2yp\nmAm2877iq3dA7TX7cXDtwYyoHcGI2hG7TI+oHcHBg6LXB9UcREWm63+OXKt92iGNfPnaOWq1i4ik\nhIp1N3Z/nGY+M2PssLGMrf04qzZ/iHaaaLXXaLPNtNkmaqsb+eDEWtZuXcuaxjUs2bCEOS/PYdO2\nPa/dNoxhNcN2LeR5hf2FNRXc/uhmWlsG46MGBN9q708fPPpTLiJSnhIt1mZ2OnADkAV+7O7XJrm/\nUslvlw/wWnCorsxyzZnHdPqf9rbWbaxrWsfaprWsaVrD2qa10XTjGtZujaaXbljK2qa1tLS37Fyx\nIvq65EXDqyowKvnQvZUcNLeGgdmBDMgOYGBF9H1AdkDX87J58wpdp5N5+fNz8yoyFV22/mcvWhX/\nvNrgkLCP8fe3XPrLhw7lUp5CyWX2olVcde9zbG6O/v/df79Kvn7W0SWNNbFibWZZ4CbgvcBK4Ekz\nu9fdlyT9O3SkAAAMqElEQVS1z1Lpul2+p6qKKg6tO5RD6w7tcrvuzqZtm1jTuIZTr59Nq22izTbx\nroMaeXx9O24tQCtnHHkw29u2s6NtB9tbo+872nawvW07W1u27jI/t1z+vDZvK+rPw7AuPwi8tGEH\nrZaBARV8f5WxbkAWMC66L8utS4dhGBnLkLEMZtF0QfPo4fJFmHfjH19gS3sbZOHRzU5jNktju3Hp\n7x5iXdvbOj60GIaZFfy9N+sY8Xq9WOfPL7zGrY++zPY258XmNl58o4LPz/orSzceyUlHDdtj+7n4\nevq6L9Z9eMk6vv3QMra1Oq+NaOWVLa9x6axXWf/m2zjt6BGdrlOseZ3FtS/z7n96Nd+4bwnbWtrZ\nVt/Gis07+NKs+TS3vp2zxo3c53iKsX6hQvlgO3vRKi7/5WJa2nce0tz0ZguX/2oxULpYLalnN5vZ\nJOAqd58av54B4O7X7G2dhoYGX7BgQSLxzJs3j8mTJyey7b5w/LVzWLW5GYBLj2nlO89En7Pq66r5\n85Wn7NO229rbdinwuxfzzublz+9s3l7Xb9vOQ8+txGnFaWFkTRurt4KbA+28Y9QQ2r0dd6fd26Np\nPNF5ude5eY6eZy7SU919GGh3yJWbrBltnlsWKjJ7fmDY27Z3md/D5QtZp7mljZ1l0Rjc+kHqWqcB\ne/5/W4y6YmYL3b2h2+USLNbnAae7+7/Gry8E3uXun9ltuenAdIDhw4dPnDlzZiLxNDU1UVtb2/2C\nZWpzcwurNjXT7s7waljXDBkz6vevpq66stTh9ciytY3saGsH6MgFYEA2w5gRg0oY2U7uTjs7i7fj\nnc7727pGdrS10o4zrMpZ3wzgVGaNw4fVRNuKi3/+el297s06+R8werPOixua4r06dQNh8/adMYwe\nut8eH2By/2/sHm8h7+fH0em6XbxfyLorXn+z4/XgAc6WHdE7AKP232+PbXS2nc721d1yXcXV2+VW\nxx/QwamthKYWOt49eEhVpzF2mkMn+95rbL1YtpD9b2jc3jFdUwFbW3cuN6x2YKfr776/gub3oqbl\nb+u1pu0d0+3uvG2/cYytmdgx75j6IR3TxagrU6ZMKahYl/wEM3e/BbgFopF1UqPf0EfWsOvxnpkr\nBpXt8Z7ubM5rh+W6BNWVWa459xgmB5bP7N1yuXXpzlxC+rfZvXNz54s7Ozc3fXLfOjd9bY8u1PKd\nudz56cBzyeuozf5M/8nl958rn1zy4wR4GXggnq6vq+azF0zueK8v60qStxtdBRyS93pUPE966ZwJ\n9fz5ylM4pn4If77ylKCKQb5zJtRzzbnHUF9XDUR/AKEVt5z+ksvlU8dQXZndZV51ZZbLp44pUUS9\np1zKUyi5XD51DJWZPVvllVkraaxJjqyfBN5qZocRFelpwEcT3J8EJHdJ3Lx583b5pBqi/pBL/kmS\n0Eh9wLfMVS7lKZRccvGk5mxwd281s88ADxFdunW7uz+X1P5EZN/0hw8dOcqlPIWSS1f31yiVRI9Z\nu/vvgN8luQ8REZH+To/IFBERKXMq1iIiImVOxVpERKTMqViLiIiUORVrERGRMqdiLSIiUuZUrEVE\nRMqcirWIiEiZU7EWEREpcyrWIiIiZS6x51n3hpltAP6e0OYPBF5LaNt9TbmUp/6SS3/JA5RLueov\nuRQjj0PdfVh3C5VVsU6SmS0o5AHfIVAu5am/5NJf8gDlUq76Sy59mYfa4CIiImVOxVpERKTMpalY\n31LqAIpIuZSn/pJLf8kDlEu56i+59FkeqTlmLSIiEqo0jaxFRESClIpibWanm9kyM3vBzK4sdTyd\nMbPbzWy9mT2bN+8AM3vYzJbH3/eP55uZ3Rjn87SZvTNvnX+Kl19uZv9UgjwOMbO5ZrbEzJ4zs88H\nnEuVmT1hZovjXL4Rzz/MzObHMf/czAbE8wfGr1+I3x+dt60Z8fxlZja1r3OJY8ia2SIzuz/wPF4x\ns2fM7CkzWxDPC+73K46hzsx+ZWbPm9lSM5sUYi5mNib+98h9vWFml4SYSxzDF+K/+WfN7J74/4LS\n/r24e7/+ArLAi8DhwABgMTC21HF1EudJwDuBZ/PmfQu4Mp6+EvjPePp9wAOAAe8G5sfzDwBeir/v\nH0/v38d5HAy8M54eBPwNGBtoLgbUxtOVwPw4xl8A0+L5PwI+HU//G/CjeHoa8PN4emz8ezcQOCz+\nfcyW4Hfsi8DdwP3x61DzeAU4cLd5wf1+xXH8FPjXeHoAUBdqLnk5ZYG1wKEh5gLUAy8D1fHrXwD/\nXOq/l5L8Y/bxD34S8FDe6xnAjFLHtZdYR7NrsV4GHBxPHwwsi6dvBs7ffTngfODmvPm7LFeinP4b\neG/ouQD7AX8F3kV0E4SK3X+/gIeASfF0Rbyc7f47l79cH8Y/CvgjcApwfxxXcHnE+32FPYt1cL9f\nwBCiomCh57Jb/KcBfw41F6JivYLoA0NF/PcytdR/L2log+d+8Dkr43khGO7ua+LptcDweHpvOZVV\nrnE7aALRiDTIXOLW8VPAeuBhok/Hm929tZO4OmKO398CDKU8crke+BLQHr8eSph5ADjwezNbaGbT\n43kh/n4dBmwA7ogPT/zYzGoIM5d804B74ungcnH3VcC3gVeBNUS//wsp8d9LGop1v+DRR7NgTt03\ns1rg18Al7v5G/nsh5eLube4+nmhkehzwthKH1GNm9n5gvbsvLHUsRXKCu78TOAO42MxOyn8zoN+v\nCqJDXz909wnAVqJWcYeAcgEgPo57NvDL3d8LJZf4uPoHiD5MjQRqgNNLGhTpKNargEPyXo+K54Vg\nnZkdDBB/Xx/P31tOZZGrmVUSFeq73H1WPDvIXHLcfTMwl6j9VWdmFZ3E1RFz/P4QYCOlz+V44Gwz\newWYSdQKv4Hw8gA6Rj64+3rgN0QfokL8/VoJrHT3+fHrXxEV7xBzyTkD+Ku7r4tfh5jLPwIvu/sG\nd28BZhH9DZX07yUNxfpJ4K3xmXwDiFo095Y4pkLdC+TOhvwnouO/ufkfj8+ofDewJW41PQScZmb7\nx58OT4vn9RkzM+A2YKm7fzfvrRBzGWZmdfF0NdGx96VERfu8eLHdc8nleB4wJx5N3AtMi88aPQx4\nK/BE32QB7j7D3Ue5+2ii3/857n4BgeUBYGY1ZjYoN030e/EsAf5+uftaYIWZjYlnnQosIcBc8pzP\nzhY4hJnLq8C7zWy/+P+z3L9Laf9e+vLAfam+iM48/BvR8cYvlzqevcR4D9HxkRaiT9yfIDru8Udg\nOfAH4IB4WQNuivN5BmjI286/AC/EXxeVII8TiFpdTwNPxV/vCzSXdwCL4lyeBb4Wzz88/qN7gajd\nNzCeXxW/fiF+//C8bX05znEZcEYJf88ms/Ns8ODyiGNeHH89l/t7DvH3K45hPLAg/h2bTXQGdKi5\n1BCNKIfkzQs1l28Az8d/9z8jOqO7pH8vuoOZiIhImUtDG1xERCRoKtYiIiJlTsVaRESkzKlYi4iI\nlDkVaxERkTKnYi1SJsysqYBlLjGz/bp4/8dmNrYX+24wsxt7sPy8+ElCT1v0xKjv565Jj9//S09j\nEJG906VbImXCzJrcvbabZV4huib1tU7ey7p7W1Lx7bavecBl7r4gvtnQNXFcJ/fF/kXSRiNrkTJj\nZpPjkWvuOcd3xXd6+hzRvYrnmtnceNkmM/uOmS0GJsXrNeS99x8WPY/7f8xseDz/QxY9p3exmT2a\nt8/cc65rzewOi54Z/bSZfbCreN19B9FDQt5iZuNy+87b7iNm9t9m9pKZXWtmF1j0nPBnzOyIRH6I\nIv2MirVIeZoAXEL0TNzDgePd/UZgNTDF3afEy9UQPQt4nLs/tts2aoD/cfdxwKPAJ+P5XwOmxvPP\n7mTfXyW6/eMx7v4OYE53wcYj+sV0/qCTccCngH8ALgSOcvfjgB8Dn+1u2yKiYi1Srp5w95Xu3k50\ny9bRe1mujeihKZ3ZQfQsXoge8Zfbxp+Bn5jZJ4FsJ+v9I9GtIAFw900Fxmx7mf+ku69x9+1Et178\nfTz/Gfael4jkUbEWKU/b86bbiB6n2JltXRynbvGdJ6V0bMPdPwV8heiJQAvNbOi+BmtmWeAYoged\n7C4/l/a81+3sPS8RyaNiLRKWRmDQvmzAzI5w9/nu/jVgA7s+xg/gYeDivOX372Z7lUQnmK1w96f3\nJTYR6ZyKtUhYbgEezJ1g1kvXxSd3PQv8hehYc76rgf1zJ6EBU/bYQuQuM8s9kawG+MA+xCQiXdCl\nWyIiImVOI2sREZEyp2ItIiJS5lSsRUREypyKtYiISJlTsRYRESlzKtYiIiJlTsVaRESkzKlYi4iI\nlLn/D9lmh5MrNSAyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a26e1cf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(1)\n",
    "ax.plot(dim, NRs[:,4],'g-', label=\"Training\")\n",
    "ax.scatter(dim, NRs[:,4])\n",
    "\n",
    "ax.set_xlabel('Intrinsic Dim')\n",
    "ax.set_ylabel('Time (second)')\n",
    "ax.set_title('Wall Clock Time')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "# ax.set_ylim([0.75,100.01])\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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